Predicted Plasma Concentrations Research Articles

Pharmacogenetics of tenofovir drug transporters in the context of HBV: Is there an impact?

BackgroundCurrent treatments for chronic hepatitis B management include orally administered nucleos(t)ide analogues, such as tenofovir (TDF), which is an acyclic adenine nucleotide analogue used both in HBV and human immune deficiency virus (HIV). The course of HBV infection is mainly dependent on viral factors, such as HBV genotypes, immunological features and host genetic variables, but a few data are available in the context of HBV, in particular for polymorphisms of genes encoding proteins involved in drug metabolism and elimination.Consequently, the aim of this study was to evaluate the potential impact of genetic variants on TDF plasma and urine concentrations in patients with HBV, considering the role of HBV genotypes. MethodsA retrospective cohort study at the Infectious Disease Unit of Amedeo di Savoia Hospital, Torino, Italy, was performed. Pharmacokinetic analyses were performed through liquidi chromatography, whereas pharmacogenetic analyses through real-time PCR. FindingsSixty - eight patients were analyzed: ABCC4 4976 C>T genetic variant showed an impact on urine TDF drug concentrations (p = 0.014). In addition, SLC22A6 453 AA was retained in the final regression multivariate model considering factors predicting plasma concentrations, while ABCC4 4976 TC/CC was the only predictor of urine concentrations in the univariate model. InterpretationIn conclusion, this is the first study showing a potential impact of genetic variants on TDF plasma and urine concentrations in the HBV context, but further studies in different and larger cohorts of patients are required.

Network pharmacology of xylazine to understand its health consequences and develop mechanistic-based remediations

The recent rise in xylazine use disorders (XUD) in humans is a significant cause for concern as a comprehensive understanding of its molecular pathology is limited, and hence, the ability to reverse the potential adverse effects is lacking. To address this gap, this study evaluates the dose-dependent impact of xylazine and its interactions with various potential targets to identify an optimal reversal strategy. A trichotomized (Low, medium, and high) dose, volume of distribution, and predicted plasma concentration of xylazine were defined. A detailed analysis of xylazine's network protein targets and their tissue-specific expression was performed using classical pharmacoinformatic tools. Molecular docking was used to assess the drug-target affinities and identify potential reversal agents. The study categorized xylazine plasma concentrations ranging from 5-8 µM, 14-20 µM, and 28-40 µM as low, medium, and high concentrations, respectively. Xylazine displayed a preferential affinity for hydrolases, kinases, transporters, and ion channels. Xylazine's network analysis revealed the following proteins: ABCC9, RET, RAPGEF4, ACHE, TGFBR1, PGR, KCNH2, KCNN2, and TRPM8 as its high-affinity targets. The tissue-specific expression of these high-affinity targets suggested potential adverse effects on various organs, particularly skeletal and smooth muscles and the adrenal gland. The study further explored the potential reversal of xylazine pharmacology using alpha2ARantagonists and CNS stimulants. Prazosin emerged as the most promising candidate, exhibiting a 200 to 2000-fold superior affinity against all high-affinity targets of xylazine. This study contributes to our understanding of xylazine's molecular mechanisms, which could be relevant to its pharmacological effects in all species, and suggests that prazosin can serve as an effective therapeutic option for mitigating xylazine-induced adverse effects in XUD patients, which warrants clinical investigation.

Use of quantitative in vitro to in vivo extrapolation (QIVIVE) for the assessment of non-combustible next-generation product aerosols.

With the use of in vitro new approach methodologies (NAMs) for the assessment of non-combustible next-generation nicotine delivery products, new extrapolation methods will also be required to interpret and contextualize the physiological relevance of these results. Quantitative in vitro to in vivo extrapolation (QIVIVE) can translate in vitro concentrations into in-life exposures with physiologically-based pharmacokinetic (PBPK) modelling and provide estimates of the likelihood of harmful effects from expected exposures. A major challenge for evaluating inhalation toxicology is an accurate assessment of the delivered dose to the surface of the cells and the internalized dose. To estimate this, we ran the multiple-path particle dosimetry (MPPD) model to characterize particle deposition in the respiratory tract and developed a PBPK model for nicotine that was validated with human clinical trial data for cigarettes. Finally, we estimated a Human Equivalent Concentration (HEC) and predicted plasma concentrations based on the minimum effective concentration (MEC) derived after acute exposure of BEAS-2B cells to cigarette smoke (1R6F), or heated tobacco product (HTP) aerosol at the air liquid interface (ALI). The MPPD-PBPK model predicted the in vivo data from clinical studies within a factor of two, indicating good agreement as noted by WHO International Programme on Chemical Safety (2010) guidance. We then used QIVIVE to derive the exposure concentration (HEC) that matched the estimated in vitro deposition point of departure (POD) (MEC cigarette = 0.38 puffs or 11.6µg nicotine, HTP = 22.9 puffs or 125.6µg nicotine) and subsequently derived the equivalent human plasma concentrations. Results indicate that for the 1R6F cigarette, inhaling 1/6th of a stick would be required to induce the same effects observed in vitro, in vivo. Whereas, for HTP it would be necessary to consume 3 sticks simultaneously to induce in vivo the effects observed in vitro. This data further demonstrates the reduced physiological potency potential of HTP aerosol compared to cigarette smoke. The QIVIVE approach demonstrates great promise in assisting human health risk assessments, however, further optimization and standardization are required for the substantiation of a meaningful contribution to tobacco harm reduction by alternative nicotine delivery products.

Using the fish plasma model to evaluate potential effects of pharmaceuticals in effluent from a large urban wastewater treatment plant

Several pharmaceuticals and personal care products (PPCPs) were evaluated using the fish plasma model (FPM) for juvenile Chinook salmon exposed to effluent from a large urban wastewater treatment plant. The FPM compares fish plasma concentrations to therapeutic values determined in human plasma as an indication of potential adverse effects. We used human Cmax values, which are the maximum plasma concentration for a minimum therapeutic dose. Observed and predicted plasma concentrations from juvenile Chinook salmon exposed to a dilution series of whole wastewater effluent were compared to 1%Cmax values to determine Response Ratios (RR) ([plasma]/1%Cmax) for assessment of possible adverse effects. Several PPCPs were found to approach or exceed an RR of 1, indicating potential effects in fish. We also predicted plasma concentrations from measured water concentrations and determined that several of the values were close to or below the analytical reporting limit (RL) indicating potential plasma concentrations for a large number of PPCPs that were below detection. Additionally, the 1%Cmax was less than the RL for several analytes, which could impede predictions of possible effect concentrations. A comparison of observed and predicted plasma concentrations found that observed values were frequently much higher than values predicted with water concentrations, especially for low log10Dow compounds. The observed versus predicted values using the human volume of distribution (Vd), were generally much closer in agreement. These data appear to support the selection of whole-body concentrations to predict plasma values, which relies more on estimating simple partitioning within the fish instead of uptake via water. Overall, these observations highlight the frequently underestimated predicted plasma concentrations and potential to cause adverse effects in fish. Using measured plasma concentrations or predicted values from whole-body concentrations along with improved prediction models and reductions in analytical detection limits will foster more accurate risk assessments of pharmaceutical exposure for fish.

A physiologically-based pharmacokinetic/pharmacodynamic modeling approach for drug-drug-gene interaction evaluation of S-warfarin with fluconazole.

Warfarin is a widely used anticoagulant, and its S-enantiomer has higher potency compared to the R-enantiomer. S-warfarin is mainly metabolized by cytochrome P450 (CYP) 2C9, and its pharmacological target is vitamin K epoxide reductase complex subunit 1 (VKORC1). Both CYP2C9 and VKORC1 have genetic polymorphisms, leading to large variations in the pharmacokinetics (PKs) and pharmacodynamics (PDs) of warfarin in the population. This makes dosage management of warfarin difficult, especially in the case of drug-drug interactions (DDIs). This study provides a whole-body physiologically-based pharmacokinetic/PD (PBPK/PD) model of S-warfarin for predicting the effects of drug-drug-gene interactions on S-warfarin PKs and PDs. The PBPK/PD model of S-warfarin was developed in PK-Sim and MoBi. Drug-dependent parameters were obtained from the literature or optimized. Of the 34 S-warfarin plasma concentration-time profiles used, 96% predicted plasma concentrations within twofold range compared to observed data. For S-warfarin plasma concentration-time profiles with CYP2C9 genotype, 364 of 386 predicted plasma concentration values (~94%) fell within the twofold of the observed values. This model was tested in DDI predictions with fluconazole as CYP2C9 perpetrators, with all predicted DDI area under the plasma concentration-time curve to the last measurable timepoint (AUClast ) ratio within twofold of the observed values. The anticoagulant effect of S-warfarin was described using an indirect response model, with all predicted international normalized ratio (INR) within twofold of the observed values. This model also incorporates a dose-adjustment method that can be used for dose adjustment and predict INR when warfarin is used in combination with CYP2C9 perpetrators.

Defining the pharmacokinetic/pharmacodynamic index of piperacillin/tazobactam within a hollow-fibre infection model to determine target attainment in intensive care patients.

It is important to optimize dosing schemes of antibiotics to maximize the probability of therapeutic success. The recommended pharmacokinetic/pharmacodynamic (PK/PD) index for piperacillin/tazobactam therapy in clinical studies ranges widely (50%-100% fT>1-4×MIC). Dosing schemes failing to achieve PK/PD targets may lead to negative treatment outcomes. The first aim of this study was to define the optimal PK/PD index of piperacillin/tazobactam with a hollow-fibre infection model (HFIM). The second aim was to predict whether these PK/PD targets are currently achieved in critically ill patients through PK/PD model simulation. A dose-fractionation study comprising 21 HFIM experiments was performed against a range of Gram-negative bacterial pathogens, doses and infusion times. Clinical data and dose histories from a case series of nine patients with a known bacterial infection treated with piperacillin/tazobactam in the ICU were collected. The PK/PD index and predicted plasma concentrations and therefore target attainment of the patients were simulated using R version 4.2.1. fT >MIC was found to be the best-fitting PK/PD index for piperacillin/tazobactam. Bactericidal activity with 2 log10 cfu reduction was associated with 77% fT>MIC. Piperacillin/tazobactam therapy was defined as clinically 'ineffective' in ∼78% (7/9) patients. Around seventy-one percent (5/7) of these patients had a probability of >10% that 2 log10 cfu reduction was not attained. Our dose-fractionation study indicates an optimal PK/PD target in piperacillin/tazobactam therapies should be 77% fT>MIC for 2 log10 kill. Doses to achieve this target should be considered when treating patients in ICU.

Physiologically Based Pharmacokinetic Modeling to Unravel the Drug-gene Interactions of Venlafaxine: Based on Activity Score-dependent Metabolism by CYP2D6 and CYP2C19 Polymorphisms.

Venlafaxine (VEN) is a commonly utilized medication for alleviating depression and anxiety disorders. The presence of genetic polymorphisms gives rise to considerable variations in plasma concentrations across different phenotypes. This divergence in phenotypic responses leads to notable differences in both the efficacy and tolerance of the drug. A physiologically based pharmacokinetic (PBPK) model for VEN and its metabolite O-desmethylvenlafaxine (ODV) to predict the impact of CYP2D6 and CYP2C19 gene polymorphisms on VEN pharmacokinetics (PK). The parent-metabolite PBPK models for VEN and ODV were developed using PK-Sim® and MoBi®. Leveraging prior research, derived and implemented CYP2D6 and CYP2C19 activity score (AS)-dependent metabolism to simulate exposure in the drug-gene interactions (DGIs) scenarios. The model's performance was evaluated by comparing predicted and observed values of plasma concentration-time (PCT) curves and PK parameters values. In the base models, 91.1%, 94.8%, and 94.6% of the predicted plasma concentrations for VEN, ODV, and VEN + ODV, respectively, fell within a twofold error range of the corresponding observed concentrations. For DGI scenarios, these values were 81.4% and 85% for VEN and ODV, respectively. Comparing CYP2D6 AS = 2 (normal metabolizers, NM) populations to AS = 0 (poor metabolizers, PM), 0.25, 0.5, 0.75, 1.0 (intermediate metabolizers, IM), 1.25, 1.5 (NM), and 3.0 (ultrarapid metabolizers, UM) populations in CYP2C19 AS = 2.0 group, the predicted DGI AUC0-96h ratios for VEN were 3.65, 3.09, 2.60, 2.18, 1.84, 1.56, 1.34, 0.61, and for ODV, they were 0.17, 0.35, 0.51, 0.64, 0.75, 0.83, 0.90, 1.11, and the results were similar in other CYP2C19 groups. It should be noted that PK differences in CYP2C19 phenotypes were not similar across different CYP2D6 groups. In clinical practice, the impact of genotyping on the in vivo disposition process of VEN should be considered to ensure the safety and efficacy of treatment.

In vitro release and bioaccessibility of oral solid preparations in a dynamic gastrointestinal system simulating fasted and fed states: A case study of metformin hydrochloride tablets

Food and formulation characteristics are crucial factors affecting the gastrointestinal release and absorption kinetics of oral solid preparations. In the present study, the dynamic continuous release and bioaccessibility of metformin hydrochloride immediate-release (IR) and sustained-release (SR) tablets were investigated in the dynamic human stomach-intestine (DHSI-IV) system simulating fasted and fed states in healthy adults. Both tablet formulations (particularly IR tablet) exhibited a postponed release in the fed state compared to the fasted state. Correspondingly, the bioaccessible fraction of metformin from IR tablets in the presence of high-fat meal was significantly reduced to 76.2 % of the fasted state. However, the in vitro bioaccessibility was less impaired by food for SR tablets with a fed/fasted ratio of 95.5 %. A convolution-based approach was used to convert in vitro bioaccessibility results to plasma concentration data. The predicted plasma concentration curve showed good agreement with human data in terms of pharmacokinetic (PK) parameters. In the fasted state, the predicted Cmax, Tmax and AUC0-24h of IR tablets were 943.9 ± 25.7 ng/mL, 2.0 ± 0.4 h and 7090.7 ± 112.0 ng.h/mL, respectively, mirroring values observed in healthy subjects. Overall, the DHSI-IV system has demonstrated potential to assess and predict the impact of meal intake on the in vivo release and absorption behaviors of oral solid preparations.

Clinical Bridging Studies and Modeling Approach for Implementation of a Patient Centric Sampling Technique in Padsevonil Clinical Development

Volumetric absorptive microsampling (VAMS) techniques have gained popularity these last years as innovative tool for collection of blood pharmacokinetic (PK) samples in clinical trials as they offer many advantages over dried blood spot and conventional venous blood sampling. The use of Mitra®, a blood collection device based on volumetric absorptive microsampling (VAMS) technology, was implemented during clinical development of padsevonil (PSL), an anti-seizure medication (ASM) candidate. The present study describes the approach used to bridge plasma (obtained from conventional venous blood sampling) and blood exposures (obtained with Mitra®) to support the use of Mitra as sole blood PK sampling method in clinical trials. Paired blood (using Mitra®) and plasma samples (using conventional venous blood sampling) were collected in healthy volunteers as well as in patients with epilepsy. PSL concentration in plasma and blood were analyzed using different approaches which included evaluation of blood-to-plasma ratios (B/P) over time, linear regression, Bland-Altman analysis as well as development of a linear-mixed effect model based on clinical pharmacology studies. Results showed that the observed in vivo B/P and the measured bias between the 2 collection methods were consistent with the measured in vitro B/P. Graphical analysis demonstrated a clear time effect on the B/P which was confirmed in the linear mixed effect model with sampling time identified as significant covariate. Finally, the built-in model was validated using independent datasets and was shown to adequately predict plasma concentration based on blood concentration with a mean bias of less than 9% (predicted versus observed plasma concentration).Graphical

Mechanistic Skin Modeling of Plasma Concentrations of Sunscreen Active Ingredients Following Facial Application

Sunscreen products constitute two distinct categories. Recreational sunscreens protect against high-intensity, episodic sun exposure, often applied over the entire body. In contrast, facial sunscreen products are designed for sub-erythemal, low-intensity daily sun exposure. Such different exposures necessitate distinctive product safety assessments. Building on earlier methods for predicting dermal disposition, a mechanistic model was developed to simulate plasma concentrations of seven organic sunscreen active ingredients: avobenzone, ensulizole, homosalate, octinoxate, octisalate, octocrylene, and oxybenzone, following facial application. In vitro permeation testing (IVPT) was performed with two different vehicles using a subset of the UV filters. These IVPT results, in addition to previously published IVPT data and published in vivo Maximal Usage Trial (MUsT) data for the UV filters, were used to train the mechanistic dermal model via a Bayesian Markov chain Monte Carlo (MCMC) method. An external validation of the trained model with real-world in vivo datasets demonstrated that the model's predicted UV filter plasma concentrations align well with experimental measurements and capture the observed inter-individual variability. Predictions of steady-state UV filter plasma concentrations under facial application scenarios at 5% concentration and at the maximal allowable concentrations were then generated by the trained model. Oxybenzone had the greatest predicted plasma concentration following facial application. Homosalate and octisalate predictions had high uncertainty associated with the absence of data. Several application scenarios pertaining to avobenzone, ensulizole, octocrylene and octinoxate were identified in which median plasma concentration levels were at 0.5 ng/ml or below when applied in the recreational or facial product. Model limitations include uncertainty in vehicle/water partitioning, formulation metamorphosis, and UV filter systemic clearance, all of which can be refined with additional data. For UV filters, limiting exposure to facial application reduces human safety concerns based on FDA established thresholds.

Empirical scaling factor for predicting human pharmacokinetic profiles of disproportionate metabolites using the Css–MRTpo method and chimeric mice with humanised livers

Predicting plasma concentration–time profiles of disproportionate metabolites in humans is crucial for evaluating metabolites according to the Safety Testing guidelines. We evaluated Css–MRTpo, an empirical method, using chimeric mice with humanised livers capable of generating human-disproportionate metabolites. Azilsartan and AZ-M2 were administered to humanised chimeric mice, and pharmacokinetic parameters were obtained. Pharmacokinetic data for DS-1971a and DS-M1 in humanised chimeric mice were obtained from the literature. The human plasma concentration–time profiles of these compounds were simulated using the Css–MRTpo method. Azilsartan, DS-1971a, and PF-04937319 produced human disproportionate metabolites, AZ-M2, DS-M1, and PF-M1, respectively. The predicted human pharmacokinetic profiles of PF-04937319 and PF-M1 were obtained from a previous study, and their outcomes were re-evaluated. Our findings revealed that the plasma concentrations of the three metabolites were unexpectedly underpredicted, whereas the three unchanged drugs were reasonably predicted. Further, the introduction of the empirical scaling factor of 3, obtained from six model compounds, improved the predictability of metabolites, suggesting the potential usefulness of the Css–MRTpo method in combination with humanised chimeric mice for predicting the pharmacokinetic profiles of disproportionate metabolites at the early stage of new drug development.

In Vitro Evaluation of the Potential for Drug Interactions by Salidroside.

Several studies utilizing Rhodiola rosea, which contains a complex mixture of phytochemicals, reported some positive drug-drug interaction (DDI) findings based on in vitro CYP450's enzyme inhibition, MAO-A and MAO-B inhibition, and preclinical pharmacokinetic studies in either rats or rabbits. However, variation in and multiplicity of constituents present in Rhodiola products is a cause for concern for accurately evaluating drug-drug interaction (DDI) risk. In this report, we examined the effects of bioengineered, nature-identical salidroside on the inhibition potential of salidroside on CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, and CYP3A4 utilizing human liver microsomes, the induction potential of salidroside on CYP1A2, CYP2B6 and CYP3A4 in cryopreserved human hepatocytes, the inhibitory potential of salidroside against recombinant human MAO-A and MAO-B, and the OATP human uptake transport inhibitory potential of salidroside using transfected HEK293-OATP1B1 and OATP1B3 cells. The results demonstrate that the bioengineered salidroside at a concentration exceeding the predicted plasma concentrations of <2 µM (based on 60 mg PO) shows no risk for drug-drug interaction due to CYP450, MAO enzymes, or OATP drug transport proteins. Our current studies further support the safe use of salidroside in combination with other drugs cleared by CYP or MAO metabolism or OATP-mediated disposition.

Predicting plasma concentration of quetiapine in patients with depression using machine learning techniques based on real-world evidence

ABSTRACT Objectives We develop a model for predicting quetiapine levels in patients with depression, using machine learning to support decisions on clinical regimens. Methods Inpatients diagnosed with depression at the First Hospital of Hebei Medical University from 1 November 2019, to 31 August were enrolled. The ratio of training cohort to testing cohort was fixed at 80%:20% for the whole dataset. Univariate analysis was executed on all information to screen the important variables influencing quetiapine TDM. The prediction abilities of nine machine learning and deep learning algorithms were compared. The prediction model was created using an algorithm with better model performance, and the model’s interpretation was done using the SHapley Additive exPlanation. Results There were 333 individuals and 412 cases of quetiapine TDM included in the study. Six significant variables were selected to establish the individualized medication model. A quetiapine concentration prediction model was created through CatBoost. In the testing cohort, the projected TDM’s accuracy was 61.45%. The prediction accuracy of quetiapine concentration within the effective range (200–750 ng/mL) was 75.47%. Conclusions This study predicts the plasma concentration of quetiapine in depression patients by machine learning, which is meaningful for the clinical medication guidance.

Semi-physiological Enriched Population Pharmacokinetic Modelling to Predict the Effects of Pregnancy on the Pharmacokinetics of Cytotoxic Drugs.

As a result of changes in physiology during pregnancy, the pharmacokinetics (PK) of drugs can be altered. It is unclear whether under- or overexposure occurs in pregnant cancer patients and thus also whether adjustments in dosing regimens are required. Given the severity of the malignant disease and the potentially high impact on both the mother and child, there is a high unmet medical need for adequate and tolerable treatment of this patient population. We aimed to develop and evaluate a semi-physiological enriched model that incorporates physiological changes during pregnancy into available population PK models developed from non-pregnant patient data. Gestational changes in plasma protein levels, renal function, hepatic function, plasma volume, extracellular water and total body water were implemented in existing empirical PK models for docetaxel, paclitaxel, epirubicin and doxorubicin. These models were used to predict PK profiles for pregnant patients, which were compared with observed data obtained from pregnant patients. The observed PK profiles were well described by the model. For docetaxel, paclitaxel and doxorubicin, an overprediction of the lower concentrations was observed, most likely as a result of a lack of data on the gestational changes in metabolizing enzymes. For paclitaxel, epirubicin and doxorubicin, the semi-physiological enriched model performed better in predicting PK in pregnant patients compared with a model that was not adjusted for pregnancy-induced changes. By incorporating gestational changes into existing population pharmacokinetic models, it is possible to adequately predict plasma concentrations of drugs in pregnant patients which may inform dose adjustments in this population.

Measuring oxytocin release in response to gavage: Computational modelling and assay validation.

In the present experiments, we tested the conclusion from previous electrophysiological experiments that gavage of sweet food and systemically applied insulin both stimulate oxytocin secretion. To do so, we measured oxytocin secretion from urethane-anaesthetised male rats, and demonstrated a significant increase in secretion in response to gavage of sweetened condensed milk but not isocaloric cream, and a significant increase in response to intravenous injection of insulin. We compared the measurements made in response to sweetened condensed milk with the predictions from a computational model, which we used to predict plasma concentrations of oxytocin from the published electrophysiological responses of oxytocin cells. The prediction from the computational model was very closely aligned to the levels of oxytocin measured in rats in response to gavage.

A novel approach for pharmacological substantiation of safety signals using plasma concentrations of medication and administrative/healthcare databases: A case study using Danish registries for an FDA warning on lamotrigine

PHARMACOM-EPI is a novel framework to predict plasma concentrations of drugs at the time of occurrence of clinical outcomes. In early 2021, the U.S. Food and Drug Administration (FDA) issued a warning on the antiseizure drug lamotrigine claiming that it has the potential to increase the risk of arrhythmias and related sudden cardiac death due to a pharmacological sodium channel-blocking effect. We hypothesized that the risk of arrhythmias and related death is due to toxicity. We used the PHARMACOM-EPI framework to investigate the relationship between lamotrigine’s plasma concentrations and the risk of death in older patients using real-world data. Danish nationwide administrative and healthcare registers were used as data sources and individuals aged 65 years or older during the period 1996 – 2018 were included in the study. According to the PHARMACOM-EPI framework, plasma concentrations of lamotrigine were predicted at the time of death and patients were categorized into non-toxic and toxic groups based on the therapeutic range of lamotrigine (3–15 mg/L). Over 1 year of treatment, the incidence rate ratio (IRR) of all-cause mortality was calculated between the propensities score matched toxic and non-toxic groups. In total, 7286 individuals were diagnosed with epilepsy and were exposed to lamotrigine, 432 of which had at least one plasma concentration measurement The pharmacometric model by Chavez et al. was used to predict lamotrigine’s plasma concentrations considering the lowest absolute percentage error among identified models (14.25 %, 95 % CI: 11.68–16.23). The majority of lamotrigine associated deaths were cardiovascular-related and occurred among individuals with plasma concentrations in the toxic range. The IRR of mortality between the toxic group and non-toxic group was 3.37 [95 % CI: 1.44–8.32] and the cumulative incidence of all-cause mortality exponentially increased in the toxic range. Application of our novel framework PHARMACOM-EPI provided strong evidence to support our hypothesis that the increased risk of all-cause and cardiovascular death was associated with a toxic plasma concentration level of lamotrigine among older lamotrigine users.

Phenome-wide association study of genetically predicted B vitamins and homocysteine biomarkers with multiple health and disease outcomes: analysis of the UK Biobank

BackgroundAlthough a number of health outcomes such as CVDs, metabolic-related outcomes, neurological disorders, pregnancy outcomes, and cancers have been identified in relation to B vitamins, evidence is of uneven quality and volume, and there is uncertainty about putative causal relationships. ObjectivesTo explore the effects of B vitamins and homocysteine on a wide range of health outcomes based on a large biorepository linking biological samples and electronic medical records. MethodsFirst, we performed a phenome-wide association study (PheWAS) to investigate the associations of genetically predicted plasma concentrations (genetic component of the circulating concentrations) of folate, vitamin B6, vitamin B12, and their metabolite homocysteine with a wide range of disease outcomes (including both prevalent and incident events) among 385,917 individuals in the UK Biobank. Second, 2-sample Mendelian randomization (MR) analysis was used to replicate any observed associations and detect causality. We considered MR P <0.05 as significant for replication. Third, dose-response, mediation, and bioinformatics analyses were carried out to examine any nonlinear trends and to disentangle the underlying mediating biological mechanisms for the identified associations. ResultsIn total, 1117 phenotypes were tested in each PheWAS analysis. After multiple corrections, 32 phenotypic associations of B vitamins and homocysteine were identified. Two-sample MR analysis supported that 3 of them were causal, including associations of higher plasma vitamin B6 with lower risk of calculus of kidney (OR: 0.64; 95% CI: 0.42, 0.97; P = 0.033), higher homocysteine concentration with higher risk of hypercholesterolemia (OR: 1.28, 95% CI: 1.04, 1.56; P = 0.018), and chronic kidney disease (OR: 1.32, 95% CI: 1.06, 1.63; P = 0.012). Significant nonlinear dose-response relationships were observed for the associations of folate with anemia, vitamin B12 with vitamin B–complex deficiencies, anemia and cholelithiasis, and homocysteine with cerebrovascular disease. ConclusionsThis study provides strong evidence for the associations of B vitamins and homocysteine with endocrine/metabolic and genitourinary disorders.

Predictive Performance of Physiologically Based Pharmacokinetic Modelling of Beta-Lactam Antibiotic Concentrations in Adipose, Bone, and Muscle Tissues.

Physiologically based pharmacokinetic (PBPK) models consist of compartments representing different tissues. As most models are only verified based on plasma concentrations, it is unclear how reliable associated tissue profiles are. This study aimed to assess the accuracy of PBPK-predicted beta-lactam antibiotic concentrations in different tissues and assess the impact of using effect site concentrations for evaluation of target attainment. Adipose, bone, and muscle concentrations of five beta-lactams (piperacillin, cefazolin, cefuroxime, ceftazidime, and meropenem) in healthy adults were collected from literature and compared with PBPK predictions. Model performance was evaluated with average fold errors (AFEs) and absolute AFEs (AAFEs) between predicted and observed concentrations. In total, 26 studies were included, 14 of which reported total tissue concentrations and 12 unbound interstitial fluid (uISF) concentrations. Concurrent plasma concentrations, used as baseline verification of the models, were fairly accurate (AFE: 1.14, AAFE: 1.50). Predicted total tissue concentrations were less accurate (AFE: 0.68, AAFE: 1.89). A slight trend for underprediction was observed but none of the studies had AFE or AAFE values outside threefold. Similarly, predictions of microdialysis-derived uISF concentrations were less accurate than plasma concentration predictions (AFE: 1.52, AAFE: 2.32). uISF concentrations tended to be overpredicted and two studies had AFEs and AAFEs outside threefold. Pharmacodynamic simulations in our case showed only a limited impact of using uISF concentrations instead of unbound plasma concentrations on target attainment rates. The results of this study illustrate the limitations of current PBPK models to predict tissue concentrations and the associated need for more accurate models. SIGNIFICANCE STATEMENT: Clinical inaccessibility of local effect site concentrations precipitates a need for predictive methods for the estimation of tissue concentrations. This is the first study in which the accuracy of PBPK-predicted tissue concentrations of beta-lactam antibiotics in humans were assessed. Predicted tissue concentrations were found to be less accurate than concurrent predicted plasma concentrations. When using PBPK models to predict tissue concentrations, this potential relative loss of accuracy should be acknowledged when clinical tissue concentrations are unavailable to verify predictions.

Physiologically based pharmacokinetic modelling and simulation to predict the plasma concentration profile of schaftoside after oral administration of total flavonoids of Desmodium styracifolium.

Introduction: The total flavonoids of Desmodium styracifolium (TFDS) are the flavonoid extracts purified from Desmodii Styracifolii Herba. The capsule of TFDS was approved for the treatment of urolithiasis by NMPA in 2022. Schaftoside is the representative compound of TFDS that possesses antilithic and antioxidant effects. The aim of this study was to develop a physiologically based pharmacokinetic (PBPK) model of schaftoside to simulate its plasma concentration profile in rat and human after oral administration of the total flavonoids of Desmodium styracifolium. Methods: The physiologically based pharmacokinetic model of schaftoside was firstly developed and verified by the pharmacokinetic data in rats following intravenous injection and oral administration of the total flavonoids of Desmodium styracifolium. Then the PBPK model was extrapolated to human with PK-Sim® software. In order to assess the accuracy of the extrapolation, a preliminary multiple-dose clinical study was performed in four healthy volunteers aged 18-45years old. The predictive performance of PBPK model was mainly evaluated by visual predictive checks and fold error of Cmax and AUC0-t of schaftoside (the ratio of predicted to observed). Finally, the adult PBPK model was scaled to several subpopulations including elderly and renally impaired patients. Results: Schaftoside underwent poor metabolism in rat and human liver microsomes in vitro, and in vivo it was extensively excreted into urine and bile as an unchanged form. By utilizing literature and experimental data, the PBPK model of schaftoside was well established in rat and human. The predicted plasma concentration profiles of schaftoside were consistent with the corresponding observed data, and the fold error values were within the 2-fold acceptance criterion. No significant pharmacokinetic differences were observed after extrapolation from adult (18-40 years old) to elderly populations (71-80years) in PK-Sim®. However, the plasma concentration of schaftoside was predicted to be much higher in renally impaired patients. The maximum steady-state plasma concentrations in patients with chronic kidney disease stage 3, 4 and 5 were 3.41, 12.32 and 23.77 times higher, respectively, than those in healthy people. Conclusion: The established PBPK model of schaftoside provided useful insight for dose selection of the total flavonoids of Desmodium styracifolium in different populations. This study provided a feasible way for the assessment of efficacy and safety of herbal medicines.

Validation of a quantitative multiplex LC-MS/MS assay of carvedilol, enalaprilat, and perindoprilat in dried blood spots from heart failure patients and its cross validation with a plasma assay

IntroductionAdherence to medication is an important determinant of outcomes in chronic diseases like heart failure. Drug assays provide objective adherence biomarkers. Dried blood spots (DBS) are appealing samples for drug assays due to less demanding transportation and storage requirements. ObjectivesTo analytically validate a LC-MS/MS method for the simultaneous quantification of carvedilol, enalaprilat, and perindoprilat in DBS and evaluate the feasibility of using the method as an adherence determining assay. To validate the assay further clinically by establishing correlation and agreement between plasma and DBS samples from a pharmacokinetic pilot study. MethodsThe method was validated over a concentration range of 1.00–200 ng/mL according to FDA guidelines. Adherence tracking ability of the assay was evaluated using a pharmacokinetic pilot study. Correlation and agreement were evaluated through Deming regression and Bland-Altman analysis, respectively. ResultsAccuracy, precision, selectivity, and sensitivity were proven with complete and reproducible extraction recovery at all concentrations tested. Stability of the analytes in the matrix and throughout sample processing was proven. The full range of concentrations of the pharmacokinetic pilot study could be quantified for enalaprilat, but not for carvedilol and perindoprilat. The difference between the observed and calculated plasma concentrations was less than 20 % of their mean for >67 % of samples for all analytes. ConclusionsThe assay is suitable as a screening tool for carvedilol and perindoprilat, while suitable as an adherence determining assay for enalaprilat. Equivalence between observed and predicted plasma concentrations proves DBS and plasma concentrations can be used interchangeably.